This study demonstrates that the magnitudes of the resulting numerical errors are sensitive to the form of the equations considered, and suggests an optimal form to minimize these errors.
Similarly, the presence of large saccades was linked to large RMS errors, but the relationship between RMS errors and the tracking dynamics is indirect because RMS errors are sensitive to a phase offset; that is, even a perfect synchrony with a constant phase would yield a large error value.
We also compared three different approaches to postcode validation (most strict, a balanced approach, most relaxed), in order to evaluate whether data linkage errors were sensitive to postcodes validation rules, ranging from most strict (postcode area, postcode district, space, postal sector, unit code) to most relaxed (spaces removed).
However, it is important to highlight assumptions (a), (b) and (c) have not been verified and are likely to be incorrect, and moreover that this estimate of the study level SMD and its standard error are sensitive to variation in these assumptions.
It is recommended that sample sizes should not consist of less than 30 comparisons as the standard error is sensitive to sample size.
This study assessed cognitive control processes using response-related brain electrical activities the error-related negativity (ERN) and error positivity (Pe)—that are sensitive to trait and state affect.
This suggests that positive prediction errors are more sensitive to increased reward predictability than are negative prediction errors.
A novel method for the robust identification of interpretable fuzzy models, based on the criterion that identification errors are least sensitive to data uncertainties and modelling errors, is suggested.
In addition, retinal thickness and blood vessel likelihood map errors are not sensitive to axial motion artifacts, while ILM and RPE position are.
The obtained analytical results verify our intuitive expectations; the hyperbolic methods are sensitive to errors in network synchronization, RRRS methods to channel modelling errors, whereas DF methods are rather insensitive to systematic errors.
Predictions from tube models can nicely complement other approaches but should be used with caution because they are sensitive to errors on the experimentally determined molecular weight and distribution.
